The use of machine learning in facilitating antibiotic discovery: A narrative review

Antimicrobial resistance (AMR) poses a growing global threat, diminishing the effectiveness of existing antibiotics and demanding innovative solutions for new drug discovery. Traditional antibiotic discovery methods are often time-consuming and inefficient, rendering them inadequate in keeping pace with the rapid evolution of pathogens. Machine learning (ML) has emerged as a transformative approach in this domain, offering enhanced computational power to analyze complex biological datasets with unprecedented speed and accuracy. This review explores the application of ML in identifying novel antibiotic targets, emphasizing its role in genomic analysis, protein function prediction, metabolic network modeling, phenotypic data integration, and drug-target interaction (DTI) prediction. ML algorithms such as support vector machines, deep learning networks, and graph-based models enable the prediction of essential genes, protein functions, and metabolic chokepoints critical for bacterial survival. These models also integrate high-throughput phenotypic screening data to identify resistance mechanisms and druggable targets. Furthermore, ML enhances the prediction of DTIs by leveraging structural and chemical data, facilitating the development of precision-targeted antibiotics. By integrating diverse datasets and continuously adapting to new information, ML-driven approaches significantly accelerate the drug discovery pipeline. This narrative review highlights the potential of ML to revolutionize antibiotic discovery and its promising role in addressing the pressing challenge of AMR.